SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 12551–12600 of 15113 papers

TitleStatusHype
Deep reinforcement learning for scheduling in large-scale networked control systems—0
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic FunctionsCode0
Reinforcement Learning for Robotics and Control with Active Uncertainty Reduction—0
Meta reinforcement learning as task inferenceCode0
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement LearningCode1
TauRieL: Targeting Traveling Salesman Problem with a deep reinforcement learning inspired architecture—0
Trajectory-Based Off-Policy Deep Reinforcement LearningCode0
Successor Options: An Option Discovery Framework for Reinforcement LearningCode0
Variational Regret Bounds for Reinforcement Learning—0
Combining Parametric and Nonparametric Models for Off-Policy Evaluation—0
Control Regularization for Reduced Variance Reinforcement LearningCode0
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks—0
Distributional Reinforcement Learning for Efficient Exploration—0
Learning and Exploiting Multiple Subgoals for Fast Exploration in Hierarchical Reinforcement Learning—0
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic ScenarioCode0
Task-Agnostic Dynamics Priors for Deep Reinforcement LearningCode0
Multi-Agent Image Classification via Reinforcement LearningCode0
Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations—0
Learning Phase Competition for Traffic Signal ControlCode0
Diagnosing Reinforcement Learning for Traffic Signal Control—0
Graph Attention Memory for Visual Navigation—0
Optimizing Routerless Network-on-Chip Designs: An Innovative Learning-Based Framework—0
Intelligent User Association for Symbiotic Radio Networks using Deep Reinforcement Learning—0
Domain Adversarial Reinforcement Learning for Partial Domain Adaptation—0
Attention-based Deep Reinforcement Learning for Multi-view Environments—0
Do Autonomous Agents Benefit from Hearing?—0
Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement LearningCode0
Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning—0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning—0
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing—0
On the Detection of Mutual Influences and Their Consideration in Reinforcement Learning Processes—0
Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action SpacesCode0
Reinforcement Learning in Non-Stationary Environments—0
Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning—0
A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem—0
Path Design for Cellular-Connected UAV with Reinforcement Learning—0
Pretrain Soft Q-Learning with Imperfect Demonstrations—0
Learning to EvolveCode0
Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning—0
Accelerated Target Updates for Q-learning—0
Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement LearningCode0
Object Exchangeability in Reinforcement Learning: Extended Abstract—0
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs—0
Continual and Multi-task Reinforcement Learning With Shared Episodic Memory—0
A Complementary Learning Systems Approach to Temporal Difference Learning—0
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement LearningCode0
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving—0
Deep Ordinal Reinforcement LearningCode0
Learning to Control in Metric Space with Optimal RegretCode0
P3O: Policy-on Policy-off Policy OptimizationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified